Wednesday, July 24, 2013

At last week’s AAPT meeting I presented a poster showing how fun and easy it is to do fluid dynamics simulations using current personal computers and software. These simulations are computationally intensive, so every bit of performance counts. On the other hand, students and hobbyists and other nonexperts like myself rarely have time to write code that will squeeze every last drop of performance out of our machines. Also, it’s hard to share code written in C or Fortran—the languages of professionals—with other nonprofessionals.

So in recent years I’ve been writing all my physics simulations in Java or Python or JavaScript. And for my poster presentation I decided to clock these languages against each other. I had already written two-dimensional lattice-Boltzmann fluid simulations in all three languages (code posted here), so all I had to do was run them with the same lattice dimensions and measure the number of calculation steps per second. Here are the benchmark results:

This algorithm performs about a hundred basic arithmetic calculations per lattice site per time step, so the Java version, at over 1000 steps per second for 16,000 lattice sites, is performing well over a billion calculations per second on my 2.3 GHz i7 MacBook Pro. I suspect that C or Fortran would run about twice as fast.

All three versions of this simulation include animated graphics, but I was careful to do the graphics in ways that didn’t significantly slow down the computation. In Java, the graphics automatically runs in a separate thread, presumably on a separate processor core.

Since my laptop has four processor cores, I could have sped up the Java version about threefold by parallelizing most of the computation. But that would violate the spirit of this test, which is to see what can be done without getting too fancy with the code. Perhaps Python can also be configured to make better use of my hardware, but I have no clue how to do so (I’m simply using the EPD Free Python installation). I did use NumPy to vectorize everything in Python, since Python loops are glacially slow.

The JavaScript results are shown for only the two fastest browsers, Chrome (version 27) and Firefox (version 22). It seems that Chrome has gotten slightly faster since my earlier tests in March, while Firefox has sped up significantly since then but still hasn’t caught up to Chrome. Both, in any case, offer remarkably good performance at more than half the speed of Java. Safari (6.0.5) is no faster than the previous version, coincidentally about the same speed as Python/NumPy. Opera (12.16) is still far too slow to bother with for this kind of simulation.

Of course, these three languages serve rather different purposes. JavaScript is for web deployment, while Python is for tinkering around on one’s own workstation. Java is fading away as a web platform, but I still like it for personal use when speed and/or interactivity is important.